{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare reference VMAF implementation and Pytorch version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import subprocess\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi']=200\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from vmaf_torch import VMAF, yuv_to_tensor, tensor_to_yuv\n",
    "\n",
    "np.set_printoptions(precision=5, suppress=True)\n",
    "torch.set_printoptions(precision=5, sci_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that elementary metrics values for frames are close (on one video) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([50, 1, 1080, 1920]), torch.Size([50, 1, 1080, 1920]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load reference and distorted yuv as tensors\n",
    "\n",
    "yuv_path_ref = \"/storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv\"\n",
    "yuv_path_dist = \"/storage/data/NFLX_dataset_public/dis/BigBuckBunny_20_288_375.yuv\"\n",
    "\n",
    "width = 1920\n",
    "height = 1080\n",
    "num_frames = 50   # using only first frames to avoid CUDA out of memory errors\n",
    "\n",
    "ref_t = yuv_to_tensor(yuv_path_ref, width, height, num_frames)\n",
    "ref_t = ref_t.to(device)\n",
    "\n",
    "dist_t = yuv_to_tensor(yuv_path_dist, width, height, num_frames)\n",
    "dist_t = dist_t.to(device)\n",
    "\n",
    "ref_t.shape, dist_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize VMAF Pytorch version\n",
    "vmaf = VMAF(temporal_pooling=True, enable_motion=True, clip_score=True, NEG=False)\n",
    "vmaf = vmaf.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize VMAF C version\n",
    "vmaf_executable = 'vmaf'       # linux\n",
    "#vmaf_executable = 'vmaf.exe'  # windows\n",
    "vmaf_model_version = \"default\"\n",
    "#vmaf_model_version = \"NEG\"\n",
    "\n",
    "class VMAF_C():\n",
    "    '''Utility class for calling reference vmaf executable'''\n",
    "\n",
    "    def __init__(self, vmaf_executable=\"vmaf\", vmaf_model_version=\"default\", verbose=True):\n",
    "        self.vmaf_executable = vmaf_executable\n",
    "        self.verbose = verbose\n",
    "\n",
    "        if vmaf_model_version == \"default\":\n",
    "            self.vmaf_model_param = \"\"\n",
    "        elif vmaf_model_version == \"NEG\":\n",
    "            self.vmaf_model_param = \"--model version=vmaf_v0.6.1neg\"\n",
    "\n",
    "    def table_from_path(self, ref_path, dist_path, width, height, num_frames):\n",
    "        vmaf_out_csv_path = 'vmaf_out.csv'\n",
    "        vmaf_param = f\"{self.vmaf_executable} -r {ref_path} -d {dist_path} -w {width} -h {height} --frame_cnt {num_frames} -p 420 -b 8 --threads 16 -q --csv -o {vmaf_out_csv_path} {self.vmaf_model_param}\"\n",
    "        if self.verbose:\n",
    "            print('Executing:', vmaf_param)\n",
    "        p = subprocess.run(vmaf_param.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "        if self.verbose:\n",
    "            print('Reading:', vmaf_out_csv_path)\n",
    "        df = pd.read_csv(vmaf_out_csv_path)\n",
    "\n",
    "        return df\n",
    "\n",
    "    def score_from_path(self, ref_path, dist_path, width, height, num_frames):\n",
    "        df = self.table_from_path(ref_path, dist_path, width, height, num_frames)\n",
    "        score = df['vmaf'].iloc[:num_frames].mean()\n",
    "\n",
    "        return score\n",
    "\n",
    "    def table_from_tensors(self, ref_tup, dist_tup):\n",
    "        # TODO check shapes and rounding here\n",
    "        ref_save_path = './ref.yuv'\n",
    "        dist_save_path = './dist.yuv'\n",
    "        width = ref_tup[0].shape[-1]\n",
    "        height = ref_tup[0].shape[-2]\n",
    "        num_frames = ref_tup[0].shape[0]\n",
    "        if self.verbose:\n",
    "            print('Saving tensors to:', ref_save_path, 'and', dist_save_path)\n",
    "        tensor_to_yuv(*ref_tup, yuv_path=ref_save_path)\n",
    "        tensor_to_yuv(*dist_tup, yuv_path=dist_save_path)\n",
    "\n",
    "        return self.table_from_path(ref_save_path, dist_save_path, width, height, num_frames)\n",
    "\n",
    "    def score_from_tensors(self, ref_tup, dist_tup):\n",
    "        df = self.table_from_tensors(ref_tup, dist_tup)\n",
    "        score = df['vmaf'].mean()\n",
    "\n",
    "        return score\n",
    "\n",
    "vmaf_c = VMAF_C(vmaf_executable=vmaf_executable, vmaf_model_version=vmaf_model_version)  # wrapper class for vmaf executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>integer_motion2</th>\n",
       "      <th>integer_motion</th>\n",
       "      <th>integer_adm2</th>\n",
       "      <th>integer_adm_scale0</th>\n",
       "      <th>integer_adm_scale1</th>\n",
       "      <th>integer_adm_scale2</th>\n",
       "      <th>integer_adm_scale3</th>\n",
       "      <th>integer_vif_scale0</th>\n",
       "      <th>integer_vif_scale1</th>\n",
       "      <th>integer_vif_scale2</th>\n",
       "      <th>integer_vif_scale3</th>\n",
       "      <th>vmaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.734756</td>\n",
       "      <td>0.815721</td>\n",
       "      <td>0.59479</td>\n",
       "      <td>0.62329</td>\n",
       "      <td>0.8353</td>\n",
       "      <td>0.252368</td>\n",
       "      <td>0.483596</td>\n",
       "      <td>0.61275</td>\n",
       "      <td>0.718086</td>\n",
       "      <td>21.099077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.879902</td>\n",
       "      <td>13.879902</td>\n",
       "      <td>0.725233</td>\n",
       "      <td>0.811577</td>\n",
       "      <td>0.584113</td>\n",
       "      <td>0.605981</td>\n",
       "      <td>0.827939</td>\n",
       "      <td>0.253677</td>\n",
       "      <td>0.480951</td>\n",
       "      <td>0.605354</td>\n",
       "      <td>0.706255</td>\n",
       "      <td>31.332096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13.960742</td>\n",
       "      <td>13.960742</td>\n",
       "      <td>0.742847</td>\n",
       "      <td>0.815371</td>\n",
       "      <td>0.592279</td>\n",
       "      <td>0.629449</td>\n",
       "      <td>0.850794</td>\n",
       "      <td>0.270983</td>\n",
       "      <td>0.516105</td>\n",
       "      <td>0.6502</td>\n",
       "      <td>0.756837</td>\n",
       "      <td>36.952862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>14.113519</td>\n",
       "      <td>14.113519</td>\n",
       "      <td>0.734082</td>\n",
       "      <td>0.81754</td>\n",
       "      <td>0.58816</td>\n",
       "      <td>0.616737</td>\n",
       "      <td>0.837131</td>\n",
       "      <td>0.267854</td>\n",
       "      <td>0.504399</td>\n",
       "      <td>0.632607</td>\n",
       "      <td>0.734851</td>\n",
       "      <td>34.348751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14.192772</td>\n",
       "      <td>14.192772</td>\n",
       "      <td>0.743509</td>\n",
       "      <td>0.820687</td>\n",
       "      <td>0.597746</td>\n",
       "      <td>0.630377</td>\n",
       "      <td>0.847964</td>\n",
       "      <td>0.276937</td>\n",
       "      <td>0.524252</td>\n",
       "      <td>0.657939</td>\n",
       "      <td>0.763557</td>\n",
       "      <td>37.611664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>14.270576</td>\n",
       "      <td>14.270576</td>\n",
       "      <td>0.73409</td>\n",
       "      <td>0.817889</td>\n",
       "      <td>0.5897</td>\n",
       "      <td>0.619923</td>\n",
       "      <td>0.834732</td>\n",
       "      <td>0.272443</td>\n",
       "      <td>0.508238</td>\n",
       "      <td>0.635047</td>\n",
       "      <td>0.736776</td>\n",
       "      <td>34.535984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>14.340849</td>\n",
       "      <td>14.340849</td>\n",
       "      <td>0.746379</td>\n",
       "      <td>0.819898</td>\n",
       "      <td>0.596499</td>\n",
       "      <td>0.639694</td>\n",
       "      <td>0.848329</td>\n",
       "      <td>0.280918</td>\n",
       "      <td>0.527029</td>\n",
       "      <td>0.659885</td>\n",
       "      <td>0.765659</td>\n",
       "      <td>38.32378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>14.413815</td>\n",
       "      <td>14.413815</td>\n",
       "      <td>0.735486</td>\n",
       "      <td>0.821692</td>\n",
       "      <td>0.593816</td>\n",
       "      <td>0.618585</td>\n",
       "      <td>0.836395</td>\n",
       "      <td>0.274136</td>\n",
       "      <td>0.508464</td>\n",
       "      <td>0.634241</td>\n",
       "      <td>0.735734</td>\n",
       "      <td>34.818417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>14.477407</td>\n",
       "      <td>14.482537</td>\n",
       "      <td>0.7453</td>\n",
       "      <td>0.823099</td>\n",
       "      <td>0.600414</td>\n",
       "      <td>0.632306</td>\n",
       "      <td>0.846213</td>\n",
       "      <td>0.283457</td>\n",
       "      <td>0.528409</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>0.763821</td>\n",
       "      <td>38.122269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>14.426366</td>\n",
       "      <td>14.477407</td>\n",
       "      <td>0.736964</td>\n",
       "      <td>0.821989</td>\n",
       "      <td>0.594767</td>\n",
       "      <td>0.62465</td>\n",
       "      <td>0.83214</td>\n",
       "      <td>0.278252</td>\n",
       "      <td>0.513779</td>\n",
       "      <td>0.639408</td>\n",
       "      <td>0.740542</td>\n",
       "      <td>35.299511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frame integer_motion2 integer_motion integer_adm2 integer_adm_scale0  \\\n",
       "0      0             0.0            0.0     0.734756           0.815721   \n",
       "1      1       13.879902      13.879902     0.725233           0.811577   \n",
       "2      2       13.960742      13.960742     0.742847           0.815371   \n",
       "3      3       14.113519      14.113519     0.734082            0.81754   \n",
       "4      4       14.192772      14.192772     0.743509           0.820687   \n",
       "5      5       14.270576      14.270576      0.73409           0.817889   \n",
       "6      6       14.340849      14.340849     0.746379           0.819898   \n",
       "7      7       14.413815      14.413815     0.735486           0.821692   \n",
       "8      8       14.477407      14.482537       0.7453           0.823099   \n",
       "9      9       14.426366      14.477407     0.736964           0.821989   \n",
       "\n",
       "  integer_adm_scale1 integer_adm_scale2 integer_adm_scale3 integer_vif_scale0  \\\n",
       "0            0.59479            0.62329             0.8353           0.252368   \n",
       "1           0.584113           0.605981           0.827939           0.253677   \n",
       "2           0.592279           0.629449           0.850794           0.270983   \n",
       "3            0.58816           0.616737           0.837131           0.267854   \n",
       "4           0.597746           0.630377           0.847964           0.276937   \n",
       "5             0.5897           0.619923           0.834732           0.272443   \n",
       "6           0.596499           0.639694           0.848329           0.280918   \n",
       "7           0.593816           0.618585           0.836395           0.274136   \n",
       "8           0.600414           0.632306           0.846213           0.283457   \n",
       "9           0.594767            0.62465            0.83214           0.278252   \n",
       "\n",
       "  integer_vif_scale1 integer_vif_scale2 integer_vif_scale3       vmaf  \n",
       "0           0.483596            0.61275           0.718086  21.099077  \n",
       "1           0.480951           0.605354           0.706255  31.332096  \n",
       "2           0.516105             0.6502           0.756837  36.952862  \n",
       "3           0.504399           0.632607           0.734851  34.348751  \n",
       "4           0.524252           0.657939           0.763557  37.611664  \n",
       "5           0.508238           0.635047           0.736776  34.535984  \n",
       "6           0.527029           0.659885           0.765659   38.32378  \n",
       "7           0.508464           0.634241           0.735734  34.818417  \n",
       "8           0.528409           0.659878           0.763821  38.122269  \n",
       "9           0.513779           0.639408           0.740542  35.299511  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run VMAF Pytorch version\n",
    "df_torch = vmaf.table(ref_t, dist_t)\n",
    "df_torch.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_20_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>integer_motion2</th>\n",
       "      <th>integer_motion</th>\n",
       "      <th>integer_adm2</th>\n",
       "      <th>integer_adm_scale0</th>\n",
       "      <th>integer_adm_scale1</th>\n",
       "      <th>integer_adm_scale2</th>\n",
       "      <th>integer_adm_scale3</th>\n",
       "      <th>integer_vif_scale0</th>\n",
       "      <th>integer_vif_scale1</th>\n",
       "      <th>integer_vif_scale2</th>\n",
       "      <th>integer_vif_scale3</th>\n",
       "      <th>vmaf</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.734757</td>\n",
       "      <td>0.815733</td>\n",
       "      <td>0.594801</td>\n",
       "      <td>0.623285</td>\n",
       "      <td>0.835292</td>\n",
       "      <td>0.252377</td>\n",
       "      <td>0.483606</td>\n",
       "      <td>0.612775</td>\n",
       "      <td>0.718224</td>\n",
       "      <td>21.106428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.879942</td>\n",
       "      <td>13.879942</td>\n",
       "      <td>0.725233</td>\n",
       "      <td>0.811572</td>\n",
       "      <td>0.584101</td>\n",
       "      <td>0.605993</td>\n",
       "      <td>0.827931</td>\n",
       "      <td>0.253683</td>\n",
       "      <td>0.480981</td>\n",
       "      <td>0.605464</td>\n",
       "      <td>0.706234</td>\n",
       "      <td>31.332317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13.960780</td>\n",
       "      <td>13.960780</td>\n",
       "      <td>0.742848</td>\n",
       "      <td>0.815400</td>\n",
       "      <td>0.592274</td>\n",
       "      <td>0.629442</td>\n",
       "      <td>0.850788</td>\n",
       "      <td>0.270979</td>\n",
       "      <td>0.516117</td>\n",
       "      <td>0.650251</td>\n",
       "      <td>0.756875</td>\n",
       "      <td>36.955908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>14.113557</td>\n",
       "      <td>14.113557</td>\n",
       "      <td>0.734084</td>\n",
       "      <td>0.817564</td>\n",
       "      <td>0.588164</td>\n",
       "      <td>0.616730</td>\n",
       "      <td>0.837124</td>\n",
       "      <td>0.267857</td>\n",
       "      <td>0.504417</td>\n",
       "      <td>0.632604</td>\n",
       "      <td>0.734903</td>\n",
       "      <td>34.351827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14.192812</td>\n",
       "      <td>14.192812</td>\n",
       "      <td>0.743503</td>\n",
       "      <td>0.820688</td>\n",
       "      <td>0.597720</td>\n",
       "      <td>0.630370</td>\n",
       "      <td>0.847957</td>\n",
       "      <td>0.276937</td>\n",
       "      <td>0.524267</td>\n",
       "      <td>0.657976</td>\n",
       "      <td>0.763615</td>\n",
       "      <td>37.613729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>14.270615</td>\n",
       "      <td>14.270615</td>\n",
       "      <td>0.734087</td>\n",
       "      <td>0.817891</td>\n",
       "      <td>0.589695</td>\n",
       "      <td>0.619919</td>\n",
       "      <td>0.834723</td>\n",
       "      <td>0.272444</td>\n",
       "      <td>0.508210</td>\n",
       "      <td>0.635091</td>\n",
       "      <td>0.736792</td>\n",
       "      <td>34.537279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>14.340888</td>\n",
       "      <td>14.340888</td>\n",
       "      <td>0.746377</td>\n",
       "      <td>0.819909</td>\n",
       "      <td>0.596479</td>\n",
       "      <td>0.639690</td>\n",
       "      <td>0.848323</td>\n",
       "      <td>0.280929</td>\n",
       "      <td>0.527048</td>\n",
       "      <td>0.659919</td>\n",
       "      <td>0.765735</td>\n",
       "      <td>38.328358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>14.413856</td>\n",
       "      <td>14.413856</td>\n",
       "      <td>0.735485</td>\n",
       "      <td>0.821702</td>\n",
       "      <td>0.593810</td>\n",
       "      <td>0.618579</td>\n",
       "      <td>0.836389</td>\n",
       "      <td>0.274142</td>\n",
       "      <td>0.508470</td>\n",
       "      <td>0.634260</td>\n",
       "      <td>0.735820</td>\n",
       "      <td>34.822351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>14.477447</td>\n",
       "      <td>14.482575</td>\n",
       "      <td>0.745297</td>\n",
       "      <td>0.823104</td>\n",
       "      <td>0.600409</td>\n",
       "      <td>0.632296</td>\n",
       "      <td>0.846206</td>\n",
       "      <td>0.283462</td>\n",
       "      <td>0.528433</td>\n",
       "      <td>0.659925</td>\n",
       "      <td>0.763922</td>\n",
       "      <td>38.127711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>14.426404</td>\n",
       "      <td>14.477447</td>\n",
       "      <td>0.736963</td>\n",
       "      <td>0.822000</td>\n",
       "      <td>0.594758</td>\n",
       "      <td>0.624646</td>\n",
       "      <td>0.832133</td>\n",
       "      <td>0.278251</td>\n",
       "      <td>0.513806</td>\n",
       "      <td>0.639499</td>\n",
       "      <td>0.740640</td>\n",
       "      <td>35.306566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frame  integer_motion2  integer_motion  integer_adm2  integer_adm_scale0  \\\n",
       "0      0         0.000000        0.000000      0.734757            0.815733   \n",
       "1      1        13.879942       13.879942      0.725233            0.811572   \n",
       "2      2        13.960780       13.960780      0.742848            0.815400   \n",
       "3      3        14.113557       14.113557      0.734084            0.817564   \n",
       "4      4        14.192812       14.192812      0.743503            0.820688   \n",
       "5      5        14.270615       14.270615      0.734087            0.817891   \n",
       "6      6        14.340888       14.340888      0.746377            0.819909   \n",
       "7      7        14.413856       14.413856      0.735485            0.821702   \n",
       "8      8        14.477447       14.482575      0.745297            0.823104   \n",
       "9      9        14.426404       14.477447      0.736963            0.822000   \n",
       "\n",
       "   integer_adm_scale1  integer_adm_scale2  integer_adm_scale3  \\\n",
       "0            0.594801            0.623285            0.835292   \n",
       "1            0.584101            0.605993            0.827931   \n",
       "2            0.592274            0.629442            0.850788   \n",
       "3            0.588164            0.616730            0.837124   \n",
       "4            0.597720            0.630370            0.847957   \n",
       "5            0.589695            0.619919            0.834723   \n",
       "6            0.596479            0.639690            0.848323   \n",
       "7            0.593810            0.618579            0.836389   \n",
       "8            0.600409            0.632296            0.846206   \n",
       "9            0.594758            0.624646            0.832133   \n",
       "\n",
       "   integer_vif_scale0  integer_vif_scale1  integer_vif_scale2  \\\n",
       "0            0.252377            0.483606            0.612775   \n",
       "1            0.253683            0.480981            0.605464   \n",
       "2            0.270979            0.516117            0.650251   \n",
       "3            0.267857            0.504417            0.632604   \n",
       "4            0.276937            0.524267            0.657976   \n",
       "5            0.272444            0.508210            0.635091   \n",
       "6            0.280929            0.527048            0.659919   \n",
       "7            0.274142            0.508470            0.634260   \n",
       "8            0.283462            0.528433            0.659925   \n",
       "9            0.278251            0.513806            0.639499   \n",
       "\n",
       "   integer_vif_scale3       vmaf  \n",
       "0            0.718224  21.106428  \n",
       "1            0.706234  31.332317  \n",
       "2            0.756875  36.955908  \n",
       "3            0.734903  34.351827  \n",
       "4            0.763615  37.613729  \n",
       "5            0.736792  34.537279  \n",
       "6            0.765735  38.328358  \n",
       "7            0.735820  34.822351  \n",
       "8            0.763922  38.127711  \n",
       "9            0.740640  35.306566  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run VMAF executable and read resulting csv table\n",
    "df_C = vmaf_c.table_from_path(yuv_path_ref, yuv_path_dist, width, height, num_frames)\n",
    "# rename some volumns so both tables have same column names\n",
    "df_C = df_C.rename(columns={s:s.removesuffix('_egl_1') for s in df_C.columns}).drop(columns='Unnamed: 13') # for NEG version\n",
    "df_C = df_C.rename(columns={s:'integer_'+s if 'integer_'not in s else s for s in df_C.columns if (s!='Frame') and (s!='vmaf')})         # for float version\n",
    "df_C.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>integer_motion2</th>\n",
       "      <th>integer_motion</th>\n",
       "      <th>integer_adm2</th>\n",
       "      <th>integer_adm_scale0</th>\n",
       "      <th>integer_adm_scale1</th>\n",
       "      <th>integer_adm_scale2</th>\n",
       "      <th>integer_adm_scale3</th>\n",
       "      <th>integer_vif_scale0</th>\n",
       "      <th>integer_vif_scale1</th>\n",
       "      <th>integer_vif_scale2</th>\n",
       "      <th>integer_vif_scale3</th>\n",
       "      <th>vmaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.00001</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.007351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.00003</td>\n",
       "      <td>-0.00011</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>-0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.003046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>-0.003076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.000037</td>\n",
       "      <td>-0.000058</td>\n",
       "      <td>-0.002065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>-0.000016</td>\n",
       "      <td>-0.001295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000034</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>-0.004578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.00001</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.003934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>-0.005442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.00004</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.000027</td>\n",
       "      <td>-0.000091</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>-0.007055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frame integer_motion2 integer_motion integer_adm2 integer_adm_scale0  \\\n",
       "0      0             0.0            0.0    -0.000001          -0.000012   \n",
       "1      0        -0.00004       -0.00004          0.0           0.000005   \n",
       "2      0       -0.000038      -0.000038    -0.000001          -0.000029   \n",
       "3      0       -0.000038      -0.000038    -0.000002          -0.000024   \n",
       "4      0        -0.00004       -0.00004     0.000006          -0.000001   \n",
       "5      0       -0.000039      -0.000039     0.000003          -0.000002   \n",
       "6      0       -0.000039      -0.000039     0.000002          -0.000011   \n",
       "7      0       -0.000041      -0.000041     0.000001           -0.00001   \n",
       "8      0        -0.00004      -0.000038     0.000003          -0.000005   \n",
       "9      0       -0.000038       -0.00004     0.000001          -0.000011   \n",
       "\n",
       "  integer_adm_scale1 integer_adm_scale2 integer_adm_scale3 integer_vif_scale0  \\\n",
       "0          -0.000011           0.000005           0.000008          -0.000009   \n",
       "1           0.000012          -0.000012           0.000008          -0.000006   \n",
       "2           0.000005           0.000007           0.000006           0.000004   \n",
       "3          -0.000004           0.000007           0.000007          -0.000003   \n",
       "4           0.000026           0.000007           0.000007                0.0   \n",
       "5           0.000005           0.000004           0.000009          -0.000001   \n",
       "6            0.00002           0.000004           0.000006          -0.000011   \n",
       "7           0.000006           0.000006           0.000006          -0.000006   \n",
       "8           0.000005            0.00001           0.000007          -0.000005   \n",
       "9           0.000009           0.000004           0.000007           0.000001   \n",
       "\n",
       "  integer_vif_scale1 integer_vif_scale2 integer_vif_scale3      vmaf  \n",
       "0           -0.00001          -0.000025          -0.000138 -0.007351  \n",
       "1           -0.00003           -0.00011           0.000021 -0.000221  \n",
       "2          -0.000012          -0.000051          -0.000038 -0.003046  \n",
       "3          -0.000018           0.000003          -0.000052 -0.003076  \n",
       "4          -0.000015          -0.000037          -0.000058 -0.002065  \n",
       "5           0.000028          -0.000044          -0.000016 -0.001295  \n",
       "6          -0.000019          -0.000034          -0.000076 -0.004578  \n",
       "7          -0.000006          -0.000019          -0.000086 -0.003934  \n",
       "8          -0.000024          -0.000047          -0.000101 -0.005442  \n",
       "9          -0.000027          -0.000091          -0.000098 -0.007055  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_difference = df_torch-df_C\n",
    "df_difference.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frame                      0.0\n",
       "integer_motion2       0.000037\n",
       "integer_motion        0.000037\n",
       "integer_adm2          0.000006\n",
       "integer_adm_scale0    0.000017\n",
       "integer_adm_scale1    0.000018\n",
       "integer_adm_scale2    0.000009\n",
       "integer_adm_scale3    0.000008\n",
       "integer_vif_scale0    0.000007\n",
       "integer_vif_scale1    0.000017\n",
       "integer_vif_scale2    0.000037\n",
       "integer_vif_scale3    0.000065\n",
       "vmaf                  0.003779\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute average difference\n",
    "df_difference.abs().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that VMAF values for videos are close on a dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to download Netflix public dataset see https://github.com/Netflix/vmaf/blob/master/resource/doc/datasets.md\n",
    "\n",
    "dataset_name = 'NFLX_public'\n",
    "yuv_fmt = 'yuv420p'\n",
    "width = 1920\n",
    "height = 1080\n",
    "\n",
    "ref_dir = '/storage/data/NFLX_dataset_public/ref'\n",
    "dis_dir = '/storage/data/NFLX_dataset_public/dis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset metadata from https://github.com/Netflix/vmaf/blob/master/resource/dataset/NFLX_dataset_public.py\n",
    "ref_videos = [\n",
    " {'content_id': 0,\n",
    "  'content_name': 'BigBuckBunny',\n",
    "  'path': ref_dir + '/BigBuckBunny_25fps.yuv'},\n",
    " {'content_id': 1,\n",
    "  'content_name': 'BirdsInCage',\n",
    "  'path': ref_dir + '/BirdsInCage_30fps.yuv'},\n",
    " {'content_id': 2,\n",
    "  'content_name': 'CrowdRun',\n",
    "  'path': ref_dir + '/CrowdRun_25fps.yuv'},\n",
    " {'content_id': 3,\n",
    "  'content_name': 'ElFuente1',\n",
    "  'path': ref_dir + '/ElFuente1_30fps.yuv'},\n",
    " {'content_id': 4,\n",
    "  'content_name': 'ElFuente2',\n",
    "  'path': ref_dir + '/ElFuente2_30fps.yuv'},\n",
    " {'content_id': 5,\n",
    "  'content_name': 'FoxBird',\n",
    "  'path': ref_dir + '/FoxBird_25fps.yuv'},\n",
    " {'content_id': 6,\n",
    "  'content_name': 'OldTownCross',\n",
    "  'path': ref_dir + '/OldTownCross_25fps.yuv'},\n",
    " {'content_id': 7,\n",
    "  'content_name': 'Seeking',\n",
    "  'path': ref_dir + '/Seeking_25fps.yuv'},\n",
    " {'content_id': 8,\n",
    "  'content_name': 'Tennis',\n",
    "  'path': ref_dir + '/Tennis_24fps.yuv'}\n",
    "]\n",
    "\n",
    "dis_videos = [{'asset_id': 0,\n",
    "  'content_id': 0,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/BigBuckBunny_25fps.yuv',\n",
    " },\n",
    " {'asset_id': 1,\n",
    "  'content_id': 1,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/BirdsInCage_30fps.yuv',\n",
    " },\n",
    " {'asset_id': 2,\n",
    "  'content_id': 2,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/CrowdRun_25fps.yuv',\n",
    " },\n",
    " {'asset_id': 3,\n",
    "  'content_id': 3,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/ElFuente1_30fps.yuv',\n",
    " },\n",
    " {'asset_id': 4,\n",
    "  'content_id': 4,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/ElFuente2_30fps.yuv',\n",
    " },\n",
    " {'asset_id': 5,\n",
    "  'content_id': 5,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/FoxBird_25fps.yuv',\n",
    " },\n",
    " {'asset_id': 6,\n",
    "  'content_id': 6,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/OldTownCross_25fps.yuv',\n",
    " },\n",
    " {'asset_id': 7,\n",
    "  'content_id': 7,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/Seeking_25fps.yuv',\n",
    " },\n",
    " {'asset_id': 8,\n",
    "  'content_id': 8,\n",
    "  'dmos': 100.0,\n",
    "  'path': ref_dir + '/Tennis_24fps.yuv',\n",
    " },\n",
    " {'asset_id': 9,\n",
    "  'content_id': 0,\n",
    "  'dmos': 22.5,\n",
    "  'path': dis_dir + '/BigBuckBunny_20_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 10,\n",
    "  'content_id': 0,\n",
    "  'dmos': 35.0,\n",
    "  'path': dis_dir + '/BigBuckBunny_30_384_550.yuv',\n",
    " },\n",
    " {'asset_id': 11,\n",
    "  'content_id': 0,\n",
    "  'dmos': 49.166666666666664,\n",
    "  'path': dis_dir + '/BigBuckBunny_40_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 12,\n",
    "  'content_id': 0,\n",
    "  'dmos': 61.666666666666664,\n",
    "  'path': dis_dir + '/BigBuckBunny_50_480_1050.yuv',\n",
    " },\n",
    " {'asset_id': 13,\n",
    "  'content_id': 0,\n",
    "  'dmos': 78.333333333333329,\n",
    "  'path': dis_dir + '/BigBuckBunny_55_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 14,\n",
    "  'content_id': 0,\n",
    "  'dmos': 97.5,\n",
    "  'path': dis_dir + '/BigBuckBunny_75_720_3050.yuv',\n",
    " },\n",
    " {'asset_id': 15,\n",
    "  'content_id': 0,\n",
    "  'dmos': 95.0,\n",
    "  'path': dis_dir + '/BigBuckBunny_80_720_4250.yuv',\n",
    " },\n",
    " {'asset_id': 16,\n",
    "  'content_id': 0,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/BigBuckBunny_85_1080_3800.yuv',\n",
    " },\n",
    " {'asset_id': 17,\n",
    "  'content_id': 0,\n",
    "  'dmos': 103.33333333333333,\n",
    "  'path': dis_dir + '/BigBuckBunny_90_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 18,\n",
    "  'content_id': 0,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/BigBuckBunny_95_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 19,\n",
    "  'content_id': 1,\n",
    "  'dmos': 38.333333333333336,\n",
    "  'path': dis_dir + '/BirdsInCage_40_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 20,\n",
    "  'content_id': 1,\n",
    "  'dmos': 40.0,\n",
    "  'path': dis_dir + '/BirdsInCage_50_288_550.yuv',\n",
    " },\n",
    " {'asset_id': 21,\n",
    "  'content_id': 1,\n",
    "  'dmos': 52.5,\n",
    "  'path': dis_dir + '/BirdsInCage_60_384_550.yuv',\n",
    " },\n",
    " {'asset_id': 22,\n",
    "  'content_id': 1,\n",
    "  'dmos': 55.0,\n",
    "  'path': dis_dir + '/BirdsInCage_65_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 23,\n",
    "  'content_id': 1,\n",
    "  'dmos': 70.0,\n",
    "  'path': dis_dir + '/BirdsInCage_80_480_750.yuv',\n",
    " },\n",
    " {'asset_id': 24,\n",
    "  'content_id': 1,\n",
    "  'dmos': 92.5,\n",
    "  'path': dis_dir + '/BirdsInCage_85_720_1050.yuv',\n",
    " },\n",
    " {'asset_id': 25,\n",
    "  'content_id': 1,\n",
    "  'dmos': 100.83333333333333,\n",
    "  'path': dis_dir + '/BirdsInCage_90_1080_1800.yuv',\n",
    " },\n",
    " {'asset_id': 26,\n",
    "  'content_id': 1,\n",
    "  'dmos': 102.5,\n",
    "  'path': dis_dir + '/BirdsInCage_95_1080_3000.yuv',\n",
    " },\n",
    " {'asset_id': 27,\n",
    "  'content_id': 2,\n",
    "  'dmos': 20.0,\n",
    "  'path': dis_dir + '/CrowdRun_03_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 28,\n",
    "  'content_id': 2,\n",
    "  'dmos': 40.0,\n",
    "  'path': dis_dir + '/CrowdRun_40_480_2350.yuv',\n",
    " },\n",
    " {'asset_id': 29,\n",
    "  'content_id': 2,\n",
    "  'dmos': 58.333333333333336,\n",
    "  'path': dis_dir + '/CrowdRun_50_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 30,\n",
    "  'content_id': 2,\n",
    "  'dmos': 67.5,\n",
    "  'path': dis_dir + '/CrowdRun_65_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 31,\n",
    "  'content_id': 2,\n",
    "  'dmos': 81.666666666666671,\n",
    "  'path': dis_dir + '/CrowdRun_75_1080_7500.yuv',\n",
    " },\n",
    " {'asset_id': 32,\n",
    "  'content_id': 2,\n",
    "  'dmos': 85.0,\n",
    "  'path': dis_dir + '/CrowdRun_80_1080_10000.yuv',\n",
    " },\n",
    " {'asset_id': 33,\n",
    "  'content_id': 2,\n",
    "  'dmos': 94.166666666666671,\n",
    "  'path': dis_dir + '/CrowdRun_90_1080_15000.yuv',\n",
    " },\n",
    " {'asset_id': 34,\n",
    "  'content_id': 3,\n",
    "  'dmos': 18.333333333333332,\n",
    "  'path': dis_dir + '/ElFuente1_10_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 35,\n",
    "  'content_id': 3,\n",
    "  'dmos': 29.166666666666668,\n",
    "  'path': dis_dir + '/ElFuente1_25_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 36,\n",
    "  'content_id': 3,\n",
    "  'dmos': 66.666666666666671,\n",
    "  'path': dis_dir + '/ElFuente1_50_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 37,\n",
    "  'content_id': 3,\n",
    "  'dmos': 72.5,\n",
    "  'path': dis_dir + '/ElFuente1_60_720_2350.yuv',\n",
    " },\n",
    " {'asset_id': 38,\n",
    "  'content_id': 3,\n",
    "  'dmos': 86.666666666666671,\n",
    "  'path': dis_dir + '/ElFuente1_70_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 39,\n",
    "  'content_id': 3,\n",
    "  'dmos': 95.0,\n",
    "  'path': dis_dir + '/ElFuente1_85_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 40,\n",
    "  'content_id': 3,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/ElFuente1_90_1080_7500.yuv',\n",
    " },\n",
    " {'asset_id': 41,\n",
    "  'content_id': 4,\n",
    "  'dmos': 25.0,\n",
    "  'path': dis_dir + '/ElFuente2_05_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 42,\n",
    "  'content_id': 4,\n",
    "  'dmos': 55.0,\n",
    "  'path': dis_dir + '/ElFuente2_30_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 43,\n",
    "  'content_id': 4,\n",
    "  'dmos': 58.333333333333336,\n",
    "  'path': dis_dir + '/ElFuente2_50_720_3050.yuv',\n",
    " },\n",
    " {'asset_id': 44,\n",
    "  'content_id': 4,\n",
    "  'dmos': 68.333333333333329,\n",
    "  'path': dis_dir + '/ElFuente2_60_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 45,\n",
    "  'content_id': 4,\n",
    "  'dmos': 75.833333333333329,\n",
    "  'path': dis_dir + '/ElFuente2_65_720_4250.yuv',\n",
    " },\n",
    " {'asset_id': 46,\n",
    "  'content_id': 4,\n",
    "  'dmos': 82.5,\n",
    "  'path': dis_dir + '/ElFuente2_70_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 47,\n",
    "  'content_id': 4,\n",
    "  'dmos': 93.333333333333329,\n",
    "  'path': dis_dir + '/ElFuente2_80_1080_10000.yuv',\n",
    " },\n",
    " {'asset_id': 48,\n",
    "  'content_id': 4,\n",
    "  'dmos': 96.666666666666671,\n",
    "  'path': dis_dir + '/ElFuente2_85_1080_15000.yuv',\n",
    " },\n",
    " {'asset_id': 49,\n",
    "  'content_id': 4,\n",
    "  'dmos': 97.5,\n",
    "  'path': dis_dir + '/ElFuente2_90_1080_20000.yuv',\n",
    " },\n",
    " {'asset_id': 50,\n",
    "  'content_id': 5,\n",
    "  'dmos': 34.166666666666664,\n",
    "  'path': dis_dir + '/FoxBird_20_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 51,\n",
    "  'content_id': 5,\n",
    "  'dmos': 60.0,\n",
    "  'path': dis_dir + '/FoxBird_40_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 52,\n",
    "  'content_id': 5,\n",
    "  'dmos': 64.166666666666671,\n",
    "  'path': dis_dir + '/FoxBird_55_480_750.yuv',\n",
    " },\n",
    " {'asset_id': 53,\n",
    "  'content_id': 5,\n",
    "  'dmos': 83.333333333333329,\n",
    "  'path': dis_dir + '/FoxBird_65_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 54,\n",
    "  'content_id': 5,\n",
    "  'dmos': 90.833333333333329,\n",
    "  'path': dis_dir + '/FoxBird_80_1080_2300.yuv',\n",
    " },\n",
    " {'asset_id': 55,\n",
    "  'content_id': 5,\n",
    "  'dmos': 101.66666666666667,\n",
    "  'path': dis_dir + '/FoxBird_95_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 56,\n",
    "  'content_id': 6,\n",
    "  'dmos': 30.833333333333332,\n",
    "  'path': dis_dir + '/OldTownCross_20_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 57,\n",
    "  'content_id': 6,\n",
    "  'dmos': 45.0,\n",
    "  'path': dis_dir + '/OldTownCross_45_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 58,\n",
    "  'content_id': 6,\n",
    "  'dmos': 57.5,\n",
    "  'path': dis_dir + '/OldTownCross_55_480_750.yuv',\n",
    " },\n",
    " {'asset_id': 59,\n",
    "  'content_id': 6,\n",
    "  'dmos': 75.0,\n",
    "  'path': dis_dir + '/OldTownCross_60_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 60,\n",
    "  'content_id': 6,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/OldTownCross_80_720_2350.yuv',\n",
    " },\n",
    " {'asset_id': 61,\n",
    "  'content_id': 6,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/OldTownCross_85_720_2950.yuv',\n",
    " },\n",
    " {'asset_id': 62,\n",
    "  'content_id': 6,\n",
    "  'dmos': 109.16666666666667,\n",
    "  'path': dis_dir + '/OldTownCross_90_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 63,\n",
    "  'content_id': 7,\n",
    "  'dmos': 19.166666666666668,\n",
    "  'path': dis_dir + '/Seeking_10_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 64,\n",
    "  'content_id': 7,\n",
    "  'dmos': 41.666666666666664,\n",
    "  'path': dis_dir + '/Seeking_30_480_1050.yuv',\n",
    " },\n",
    " {'asset_id': 65,\n",
    "  'content_id': 7,\n",
    "  'dmos': 50.833333333333336,\n",
    "  'path': dis_dir + '/Seeking_45_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 66,\n",
    "  'content_id': 7,\n",
    "  'dmos': 66.666666666666671,\n",
    "  'path': dis_dir + '/Seeking_50_720_2350.yuv',\n",
    " },\n",
    " {'asset_id': 67,\n",
    "  'content_id': 7,\n",
    "  'dmos': 75.833333333333329,\n",
    "  'path': dis_dir + '/Seeking_60_720_3050.yuv',\n",
    " },\n",
    " {'asset_id': 68,\n",
    "  'content_id': 7,\n",
    "  'dmos': 80.833333333333329,\n",
    "  'path': dis_dir + '/Seeking_65_1080_4300.yuv',\n",
    " },\n",
    " {'asset_id': 69,\n",
    "  'content_id': 7,\n",
    "  'dmos': 91.666666666666671,\n",
    "  'path': dis_dir + '/Seeking_75_1080_5800.yuv',\n",
    " },\n",
    " {'asset_id': 70,\n",
    "  'content_id': 7,\n",
    "  'dmos': 90.0,\n",
    "  'path': dis_dir + '/Seeking_85_1080_7500.yuv',\n",
    " },\n",
    " {'asset_id': 71,\n",
    "  'content_id': 7,\n",
    "  'dmos': 91.666666666666671,\n",
    "  'path': dis_dir + '/Seeking_90_1080_15000.yuv',\n",
    " },\n",
    " {'asset_id': 72,\n",
    "  'content_id': 7,\n",
    "  'dmos': 96.666666666666671,\n",
    "  'path': dis_dir + '/Seeking_95_1080_20000.yuv',\n",
    " },\n",
    " {'asset_id': 73,\n",
    "  'content_id': 8,\n",
    "  'dmos': 33.333333333333336,\n",
    "  'path': dis_dir + '/Tennis_20_288_375.yuv',\n",
    " },\n",
    " {'asset_id': 74,\n",
    "  'content_id': 8,\n",
    "  'dmos': 50.0,\n",
    "  'path': dis_dir + '/Tennis_40_384_750.yuv',\n",
    " },\n",
    " {'asset_id': 75,\n",
    "  'content_id': 8,\n",
    "  'dmos': 71.666666666666671,\n",
    "  'path': dis_dir + '/Tennis_60_480_1050.yuv',\n",
    " },\n",
    " {'asset_id': 76,\n",
    "  'content_id': 8,\n",
    "  'dmos': 68.333333333333329,\n",
    "  'path': dis_dir + '/Tennis_70_480_1750.yuv',\n",
    " },\n",
    " {'asset_id': 77,\n",
    "  'content_id': 8,\n",
    "  'dmos': 94.166666666666671,\n",
    "  'path': dis_dir + '/Tennis_80_720_3050.yuv',\n",
    " },\n",
    " {'asset_id': 78,\n",
    "  'content_id': 8,\n",
    "  'dmos': 99.166666666666671,\n",
    "  'path': dis_dir + '/Tennis_90_1080_4300.yuv',\n",
    " }]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_20_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_30_384_550.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_40_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_50_480_1050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_55_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_75_720_3050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_80_720_4250.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_85_1080_3800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_90_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BigBuckBunny_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/BigBuckBunny_95_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_40_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_50_288_550.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_60_384_550.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_65_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_80_480_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_85_720_1050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_90_1080_1800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/BirdsInCage_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/BirdsInCage_95_1080_3000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_03_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_40_480_2350.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_50_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_65_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_75_1080_7500.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_80_1080_10000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/CrowdRun_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/CrowdRun_90_1080_15000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_10_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_25_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_50_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_60_720_2350.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_70_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_85_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente1_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente1_90_1080_7500.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_05_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_30_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_50_720_3050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_60_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_65_720_4250.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_70_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_80_1080_10000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_85_1080_15000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/ElFuente2_30fps.yuv -d /storage/data/NFLX_dataset_public/dis/ElFuente2_90_1080_20000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_20_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_40_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_55_480_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_65_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_80_1080_2300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/FoxBird_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/FoxBird_95_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_20_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_45_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_55_480_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_60_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_80_720_2350.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_85_720_2950.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/OldTownCross_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/OldTownCross_90_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_10_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_30_480_1050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_45_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_50_720_2350.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_60_720_3050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_65_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_75_1080_5800.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_85_1080_7500.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_90_1080_15000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Seeking_25fps.yuv -d /storage/data/NFLX_dataset_public/dis/Seeking_95_1080_20000.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_20_288_375.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_40_384_750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_60_480_1050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_70_480_1750.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_80_720_3050.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n",
      "Executing: vmaf -r /storage/data/NFLX_dataset_public/ref/Tennis_24fps.yuv -d /storage/data/NFLX_dataset_public/dis/Tennis_90_1080_4300.yuv -w 1920 -h 1080 --frame_cnt 50 -p 420 -b 8 --threads 16 -q --csv -o vmaf_out.csv \n",
      "Reading: vmaf_out.csv\n"
     ]
    }
   ],
   "source": [
    "num_frames = 50   # using only first frames to avoid CUDA out of memory errors\n",
    "\n",
    "c_vmaf_scores = []\n",
    "torch_vmaf_scores = []\n",
    "\n",
    "feature_names = ['integer_motion2', 'integer_motion', 'integer_adm2',\n",
    "'integer_adm_scale0', 'integer_adm_scale1', 'integer_adm_scale2',\n",
    "'integer_adm_scale3', 'integer_vif_scale0', 'integer_vif_scale1',\n",
    "'integer_vif_scale2', 'integer_vif_scale3']\n",
    "\n",
    "c_features = {f_n:[] for f_n in feature_names}\n",
    "torch_features = {f_n:[] for f_n in feature_names}\n",
    "\n",
    "for vid in dis_videos:\n",
    "    yuv_path_dist = vid['path']\n",
    "    yuv_path_ref = [d['path'] for d in ref_videos if d['content_id']==vid['content_id']][0]\n",
    "    \n",
    "    df_C = vmaf_c.table_from_path(yuv_path_ref, yuv_path_dist, width, height, num_frames)\n",
    "    df_C = df_C.rename(columns={s:s.removesuffix('_egl_1') for s in df_C.columns}).drop(columns='Unnamed: 13') # for NEG version\n",
    "    df_C = df_C.rename(columns={s:'integer_'+s if 'integer_'not in s else s for s in df_C.columns if (s!='Frame') and (s!='vmaf')}) # for float version\n",
    "    c_score = df_C['vmaf'].mean()\n",
    "    c_vmaf_scores.append(c_score)\n",
    "    \n",
    "    ref_t = yuv_to_tensor(yuv_path_ref, width, height, num_frames)\n",
    "    ref_t = ref_t.to(device)\n",
    "    dist_t = yuv_to_tensor(yuv_path_dist, width, height, num_frames)\n",
    "    dist_t = dist_t.to(device)\n",
    "    with torch.no_grad():\n",
    "        #torch_score = vmaf(ref_t, dist_t).item()\n",
    "        df_torch = vmaf.table(ref_t, dist_t)\n",
    "    torch_score = df_torch['vmaf'].mean()\n",
    "    torch_vmaf_scores.append(torch_score)\n",
    "    \n",
    "    for f_n in feature_names:\n",
    "        c_features[f_n].extend(df_C[f_n].to_list())\n",
    "        torch_features[f_n].extend(df_torch[f_n].to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[99.94856 98.25662 99.94856 99.94855 99.94857 98.66826 99.94856 99.94857\n",
      " 99.94855 38.77267 53.45405 59.57528 71.26628 79.80366 92.59078 95.89494\n",
      " 99.36421 99.64995 99.72615 47.8713  49.20007 61.95285 62.95595 73.04774\n",
      " 83.67003 92.03811 93.10625  9.50615 45.24235 67.25528 75.8502  82.62579\n",
      " 88.94848 95.46937 18.96033 34.78473 59.81088 71.02117 87.90463 94.42793\n",
      " 98.31813  8.17311 27.51911 38.36571 43.87356 45.95208 52.97681 70.6065\n",
      " 84.31544 92.40967 23.62585 47.11207 56.70465 69.9516  90.85483 96.77523\n",
      " 24.15121 44.36051 56.5914  62.48474 79.74344 80.52022 93.08052 14.99169\n",
      " 38.1423  49.40092 58.76257 65.61184 76.02255 83.90921 90.03208 98.87703\n",
      " 99.5548  48.70014 66.91183 77.35461 83.50241 92.32154 97.5599 ]\n",
      "[99.94855 98.25633 99.94857 99.94852 99.94856 98.6682  99.94856 99.94858\n",
      " 99.94855 38.76918 53.44974 59.57076 71.26163 79.79903 92.58531 95.89039\n",
      " 99.36157 99.64983 99.72594 47.86439 49.19118 61.94929 62.95972 73.03864\n",
      " 83.66441 92.03106 93.10261  9.50634 45.24839 67.2621  75.85646 82.63385\n",
      " 88.95419 95.47536 18.96242 34.78791 59.81357 71.02469 87.90725 94.43022\n",
      " 98.32023  8.17409 27.51804 38.3643  43.8732  45.95019 52.97358 70.60147\n",
      " 84.30681 92.40347 23.62639 47.1101  56.70267 69.94661 90.8589  96.77441\n",
      " 24.17047 44.3832  56.61804 62.50987 79.77009 80.54612 93.10773 14.99149\n",
      " 38.14014 49.39861 58.75997 65.6092  76.01957 83.90655 90.02888 98.87593\n",
      " 99.55444 48.72449 66.93543 77.37795 83.52314 92.34312 97.56931]\n"
     ]
    }
   ],
   "source": [
    "c_scores = np.array(c_vmaf_scores)\n",
    "torch_scores = np.array(torch_vmaf_scores)\n",
    "\n",
    "print(c_scores)\n",
    "print(torch_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcb3614f650>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(np.arange(79), c_scores, 's-', label='Official C implementation')\n",
    "plt.plot(np.arange(79), torch_scores, 'o--', label='PyTorch implementation')\n",
    "plt.xlabel('Video')\n",
    "plt.ylabel('VMAF score')\n",
    "plt.yticks(np.arange(0,101, 10), labels=[str(x).rjust(6, ' ') for x in np.arange(0,101, 10)])  # make both plots the same size\n",
    "plt.legend(loc=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00001, 0.00029, 0.00001, 0.00004, 0.     , 0.00007, 0.00001,\n",
       "       0.00001, 0.     , 0.00349, 0.00431, 0.00452, 0.00465, 0.00464,\n",
       "       0.00547, 0.00455, 0.00264, 0.00012, 0.00021, 0.00691, 0.00889,\n",
       "       0.00357, 0.00377, 0.00911, 0.00562, 0.00705, 0.00364, 0.00019,\n",
       "       0.00604, 0.00683, 0.00626, 0.00806, 0.00571, 0.00599, 0.00209,\n",
       "       0.00318, 0.00269, 0.00352, 0.00262, 0.00229, 0.0021 , 0.00098,\n",
       "       0.00106, 0.00141, 0.00036, 0.00189, 0.00323, 0.00503, 0.00862,\n",
       "       0.0062 , 0.00053, 0.00197, 0.00198, 0.00498, 0.00407, 0.00082,\n",
       "       0.01926, 0.02269, 0.02664, 0.02512, 0.02664, 0.02589, 0.02721,\n",
       "       0.0002 , 0.00216, 0.0023 , 0.0026 , 0.00265, 0.00298, 0.00266,\n",
       "       0.0032 , 0.0011 , 0.00036, 0.02435, 0.0236 , 0.02334, 0.02073,\n",
       "       0.02158, 0.00941])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dif = np.abs(torch_scores - c_scores)\n",
    "dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute difference in VMAF scores')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(79), dif)\n",
    "plt.xlabel('Video')\n",
    "plt.ylabel('Absolute difference in VMAF scores')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0063415146650974965,\n",
       " 0.007872519084080359,\n",
       " 8.701367164576368e-07,\n",
       " 0.02720559372069431)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dif.mean(), dif.std(), dif.min(), dif.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integer_motion2 2.10227016120057e-05\n",
      "integer_motion 2.0914144144141918e-05\n",
      "integer_adm2 6.742017304140894e-06\n",
      "integer_adm_scale0 1.4823751319208265e-05\n",
      "integer_adm_scale1 1.4953979398993844e-05\n",
      "integer_adm_scale2 1.168075469583722e-05\n",
      "integer_adm_scale3 8.810439298604325e-06\n",
      "integer_vif_scale0 2.9370299166002973e-05\n",
      "integer_vif_scale1 3.162750494196396e-05\n",
      "integer_vif_scale2 5.1258400964131346e-05\n",
      "integer_vif_scale3 0.00010490992777425858\n"
     ]
    }
   ],
   "source": [
    "for f_n in feature_names:\n",
    "    f_dif = np.abs(np.array(c_features[f_n]) - np.array(torch_features[f_n]))\n",
    "    print(f_n, f_dif.mean())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "th",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
